Overview

Dataset statistics

Number of variables16
Number of observations46527
Missing cells21475
Missing cells (%)2.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.4 MiB
Average record size in memory503.9 B

Variable types

Numeric10
Text3
Categorical2
DateTime1

Alerts

id is highly overall correlated with host_idHigh correlation
host_id is highly overall correlated with idHigh correlation
latitude is highly overall correlated with neighbourhood_groupHigh correlation
longitude is highly overall correlated with neighbourhood_groupHigh correlation
number_of_reviews is highly overall correlated with reviews_per_monthHigh correlation
reviews_per_month is highly overall correlated with number_of_reviewsHigh correlation
neighbourhood_group is highly overall correlated with latitude and 1 other fieldsHigh correlation
last_review has 10711 (23.0%) missing valuesMissing
reviews_per_month has 10711 (23.0%) missing valuesMissing
price is highly skewed (γ1 = 21.55255255)Skewed
id has unique valuesUnique
number_of_reviews has 10711 (23.0%) zerosZeros
availability_365 has 19967 (42.9%) zerosZeros

Reproduction

Analysis started2023-09-06 02:53:00.246640
Analysis finished2023-09-06 02:53:10.871117
Duration10.62 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct46527
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22998909
Minimum2595
Maximum44818009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size363.6 KiB
2023-09-05T22:53:10.931068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2595
5-th percentile1567876.3
Q110625576
median22205277
Q335754400
95-th percentile43273246
Maximum44818009
Range44815414
Interquartile range (IQR)25128824

Descriptive statistics

Standard deviation13731230
Coefficient of variation (CV)0.59703832
Kurtosis-1.2904723
Mean22998909
Median Absolute Deviation (MAD)12472050
Skewness-0.022291119
Sum1.0700702 × 1012
Variance1.8854667 × 1014
MonotonicityStrictly increasing
2023-09-05T22:53:11.021720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2595 1
 
< 0.1%
31221559 1
 
< 0.1%
31221996 1
 
< 0.1%
31223352 1
 
< 0.1%
31223941 1
 
< 0.1%
31224224 1
 
< 0.1%
31224372 1
 
< 0.1%
31224656 1
 
< 0.1%
31225319 1
 
< 0.1%
31225706 1
 
< 0.1%
Other values (46517) 46517
> 99.9%
ValueCountFrequency (%)
2595 1
< 0.1%
3831 1
< 0.1%
5121 1
< 0.1%
5136 1
< 0.1%
5178 1
< 0.1%
5203 1
< 0.1%
5238 1
< 0.1%
5552 1
< 0.1%
5803 1
< 0.1%
6021 1
< 0.1%
ValueCountFrequency (%)
44818009 1
< 0.1%
44814944 1
< 0.1%
44811717 1
< 0.1%
44807786 1
< 0.1%
44807522 1
< 0.1%
44807338 1
< 0.1%
44804690 1
< 0.1%
44803201 1
< 0.1%
44802224 1
< 0.1%
44802032 1
< 0.1%

name
Text

Distinct45315
Distinct (%)97.4%
Missing18
Missing (%)< 0.1%
Memory size4.3 MiB
2023-09-05T22:53:11.182429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length161
Median length84
Mean length37.0699
Min length1

Characters and Unicode

Total characters1724084
Distinct characters821
Distinct categories21 ?
Distinct scripts11 ?
Distinct blocks23 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44557 ?
Unique (%)95.8%

Sample

1st rowSkylit Midtown Castle
2nd rowWhole flr w/private bdrm, bath & kitchen(pls read)
3rd rowBlissArtsSpace!
4th rowSpacious Brooklyn Duplex, Patio + Garden
5th rowLarge Furnished Room Near B'way 
ValueCountFrequency (%)
in 15849
 
5.6%
room 9555
 
3.4%
7592
 
2.7%
bedroom 7205
 
2.5%
private 6640
 
2.3%
apartment 6582
 
2.3%
cozy 4770
 
1.7%
apt 4244
 
1.5%
the 3875
 
1.4%
studio 3824
 
1.3%
Other values (12473) 214895
75.4%
2023-09-05T22:53:11.469263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
239964
 
13.9%
e 121301
 
7.0%
o 117959
 
6.8%
t 101776
 
5.9%
a 100450
 
5.8%
r 93857
 
5.4%
i 91748
 
5.3%
n 91492
 
5.3%
l 50822
 
2.9%
m 47502
 
2.8%
Other values (811) 667213
38.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1167051
67.7%
Uppercase Letter 245781
 
14.3%
Space Separator 239980
 
13.9%
Other Punctuation 32115
 
1.9%
Decimal Number 23719
 
1.4%
Dash Punctuation 6420
 
0.4%
Math Symbol 2163
 
0.1%
Other Letter 2091
 
0.1%
Close Punctuation 1439
 
0.1%
Open Punctuation 1357
 
0.1%
Other values (11) 1968
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
71
 
3.4%
48
 
2.3%
35
 
1.7%
35
 
1.7%
33
 
1.6%
33
 
1.6%
29
 
1.4%
28
 
1.3%
27
 
1.3%
24
 
1.1%
Other values (473) 1728
82.6%
Other Symbol
ValueCountFrequency (%)
336
30.8%
162
14.8%
110
 
10.1%
54
 
4.9%
46
 
4.2%
22
 
2.0%
21
 
1.9%
21
 
1.9%
18
 
1.6%
15
 
1.4%
Other values (115) 287
26.3%
Lowercase Letter
ValueCountFrequency (%)
e 121301
 
10.4%
o 117959
 
10.1%
t 101776
 
8.7%
a 100450
 
8.6%
r 93857
 
8.0%
i 91748
 
7.9%
n 91492
 
7.8%
l 50822
 
4.4%
m 47502
 
4.1%
s 46745
 
4.0%
Other values (57) 303399
26.0%
Uppercase Letter
ValueCountFrequency (%)
B 27625
 
11.2%
S 24027
 
9.8%
C 19855
 
8.1%
A 17892
 
7.3%
R 16015
 
6.5%
P 13770
 
5.6%
L 12438
 
5.1%
E 12085
 
4.9%
M 11088
 
4.5%
N 10646
 
4.3%
Other values (43) 80340
32.7%
Other Punctuation
ValueCountFrequency (%)
, 9057
28.2%
! 6761
21.1%
/ 5081
15.8%
. 4425
13.8%
& 3160
 
9.8%
' 1031
 
3.2%
* 782
 
2.4%
: 565
 
1.8%
# 508
 
1.6%
" 261
 
0.8%
Other values (15) 484
 
1.5%
Decimal Number
ValueCountFrequency (%)
1 7891
33.3%
2 6413
27.0%
3 2487
 
10.5%
0 2067
 
8.7%
5 2003
 
8.4%
4 1271
 
5.4%
6 468
 
2.0%
8 425
 
1.8%
7 410
 
1.7%
9 278
 
1.2%
Other values (4) 6
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
+ 1040
48.1%
| 621
28.7%
~ 309
 
14.3%
= 90
 
4.2%
< 43
 
2.0%
> 39
 
1.8%
13
 
0.6%
4
 
0.2%
2
 
0.1%
1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 1362
94.6%
] 37
 
2.6%
24
 
1.7%
} 9
 
0.6%
5
 
0.3%
2
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 1281
94.4%
[ 37
 
2.7%
23
 
1.7%
{ 9
 
0.7%
5
 
0.4%
2
 
0.1%
Modifier Symbol
ValueCountFrequency (%)
^ 7
38.9%
´ 5
27.8%
` 3
16.7%
🏻 1
 
5.6%
🏼 1
 
5.6%
🏾 1
 
5.6%
Dash Punctuation
ValueCountFrequency (%)
- 6353
99.0%
39
 
0.6%
25
 
0.4%
2
 
< 0.1%
1
 
< 0.1%
Nonspacing Mark
ValueCountFrequency (%)
208
90.8%
18
 
7.9%
͠ 2
 
0.9%
͜ 1
 
0.4%
Space Separator
ValueCountFrequency (%)
239964
> 99.9%
  9
 
< 0.1%
  7
 
< 0.1%
Final Punctuation
ValueCountFrequency (%)
237
83.5%
47
 
16.5%
Control
ValueCountFrequency (%)
148
99.3%
1
 
0.7%
Initial Punctuation
ValueCountFrequency (%)
44
86.3%
7
 
13.7%
Connector Punctuation
ValueCountFrequency (%)
_ 32
97.0%
1
 
3.0%
Other Number
ValueCountFrequency (%)
² 9
81.8%
½ 2
 
18.2%
Currency Symbol
ValueCountFrequency (%)
$ 81
100.0%
Modifier Letter
ValueCountFrequency (%)
12
100.0%
Format
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1412559
81.9%
Common 308929
 
17.9%
Han 1775
 
0.1%
Cyrillic 273
 
< 0.1%
Inherited 237
 
< 0.1%
Katakana 155
 
< 0.1%
Hiragana 75
 
< 0.1%
Hebrew 40
 
< 0.1%
Hangul 39
 
< 0.1%
Georgian 1
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
71
 
4.0%
48
 
2.7%
35
 
2.0%
35
 
2.0%
33
 
1.9%
33
 
1.9%
29
 
1.6%
28
 
1.6%
27
 
1.5%
24
 
1.4%
Other values (360) 1412
79.5%
Common
ValueCountFrequency (%)
239964
77.7%
, 9057
 
2.9%
1 7891
 
2.6%
! 6761
 
2.2%
2 6413
 
2.1%
- 6353
 
2.1%
/ 5081
 
1.6%
. 4425
 
1.4%
& 3160
 
1.0%
3 2487
 
0.8%
Other values (203) 17337
 
5.6%
Latin
ValueCountFrequency (%)
e 121301
 
8.6%
o 117959
 
8.4%
t 101776
 
7.2%
a 100450
 
7.1%
r 93857
 
6.6%
i 91748
 
6.5%
n 91492
 
6.5%
l 50822
 
3.6%
m 47502
 
3.4%
s 46745
 
3.3%
Other values (67) 548907
38.9%
Cyrillic
ValueCountFrequency (%)
а 35
 
12.8%
н 24
 
8.8%
о 22
 
8.1%
т 20
 
7.3%
е 15
 
5.5%
р 14
 
5.1%
к 13
 
4.8%
я 9
 
3.3%
м 9
 
3.3%
с 8
 
2.9%
Other values (33) 104
38.1%
Katakana
ValueCountFrequency (%)
19
 
12.3%
11
 
7.1%
10
 
6.5%
8
 
5.2%
8
 
5.2%
8
 
5.2%
7
 
4.5%
6
 
3.9%
5
 
3.2%
5
 
3.2%
Other values (31) 68
43.9%
Hangul
ValueCountFrequency (%)
2
 
5.1%
2
 
5.1%
2
 
5.1%
1
 
2.6%
1
 
2.6%
1
 
2.6%
1
 
2.6%
1
 
2.6%
1
 
2.6%
1
 
2.6%
Other values (26) 26
66.7%
Hiragana
ValueCountFrequency (%)
16
21.3%
9
12.0%
8
10.7%
7
9.3%
6
 
8.0%
4
 
5.3%
4
 
5.3%
3
 
4.0%
3
 
4.0%
2
 
2.7%
Other values (10) 13
17.3%
Hebrew
ValueCountFrequency (%)
ו 7
17.5%
ר 5
12.5%
ב 5
12.5%
ש 5
12.5%
י 4
10.0%
מ 3
7.5%
ת 3
7.5%
ה 2
 
5.0%
ל 1
 
2.5%
ד 1
 
2.5%
Other values (4) 4
10.0%
Inherited
ValueCountFrequency (%)
208
87.8%
18
 
7.6%
8
 
3.4%
͠ 2
 
0.8%
͜ 1
 
0.4%
Georgian
ValueCountFrequency (%)
1
100.0%
Arabic
ValueCountFrequency (%)
٪ 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1719584
99.7%
CJK 1775
 
0.1%
Misc Symbols 593
 
< 0.1%
None 500
 
< 0.1%
Punctuation 452
 
< 0.1%
Dingbats 310
 
< 0.1%
Cyrillic 273
 
< 0.1%
VS 226
 
< 0.1%
Katakana 169
 
< 0.1%
Hiragana 75
 
< 0.1%
Other values (13) 127
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
239964
 
14.0%
e 121301
 
7.1%
o 117959
 
6.9%
t 101776
 
5.9%
a 100450
 
5.8%
r 93857
 
5.5%
i 91748
 
5.3%
n 91492
 
5.3%
l 50822
 
3.0%
m 47502
 
2.8%
Other values (87) 662713
38.5%
Misc Symbols
ValueCountFrequency (%)
336
56.7%
110
 
18.5%
46
 
7.8%
22
 
3.7%
21
 
3.5%
14
 
2.4%
9
 
1.5%
5
 
0.8%
4
 
0.7%
3
 
0.5%
Other values (15) 23
 
3.9%
Punctuation
ValueCountFrequency (%)
237
52.4%
47
 
10.4%
44
 
9.7%
43
 
9.5%
39
 
8.6%
25
 
5.5%
8
 
1.8%
7
 
1.5%
1
 
0.2%
1
 
0.2%
VS
ValueCountFrequency (%)
208
92.0%
18
 
8.0%
Dingbats
ValueCountFrequency (%)
162
52.3%
21
 
6.8%
18
 
5.8%
15
 
4.8%
13
 
4.2%
12
 
3.9%
10
 
3.2%
7
 
2.3%
6
 
1.9%
6
 
1.9%
Other values (15) 40
 
12.9%
CJK
ValueCountFrequency (%)
71
 
4.0%
48
 
2.7%
35
 
2.0%
35
 
2.0%
33
 
1.9%
33
 
1.9%
29
 
1.6%
28
 
1.6%
27
 
1.5%
24
 
1.4%
Other values (360) 1412
79.5%
None
ValueCountFrequency (%)
54
 
10.8%
ó 46
 
9.2%
33
 
6.6%
à 27
 
5.4%
24
 
4.8%
23
 
4.6%
· 17
 
3.4%
🌟 15
 
3.0%
é 13
 
2.6%
13
 
2.6%
Other values (102) 235
47.0%
Cyrillic
ValueCountFrequency (%)
а 35
 
12.8%
н 24
 
8.8%
о 22
 
8.1%
т 20
 
7.3%
е 15
 
5.5%
р 14
 
5.1%
к 13
 
4.8%
я 9
 
3.3%
м 9
 
3.3%
с 8
 
2.9%
Other values (33) 104
38.1%
Katakana
ValueCountFrequency (%)
19
 
11.2%
12
 
7.1%
11
 
6.5%
10
 
5.9%
8
 
4.7%
8
 
4.7%
8
 
4.7%
7
 
4.1%
6
 
3.6%
5
 
3.0%
Other values (33) 75
44.4%
Hiragana
ValueCountFrequency (%)
16
21.3%
9
12.0%
8
10.7%
7
9.3%
6
 
8.0%
4
 
5.3%
4
 
5.3%
3
 
4.0%
3
 
4.0%
2
 
2.7%
Other values (10) 13
17.3%
Arrows
ValueCountFrequency (%)
13
92.9%
1
 
7.1%
Hebrew
ValueCountFrequency (%)
ו 7
17.5%
ר 5
12.5%
ב 5
12.5%
ש 5
12.5%
י 4
10.0%
מ 3
7.5%
ת 3
7.5%
ה 2
 
5.0%
ל 1
 
2.5%
ד 1
 
2.5%
Other values (4) 4
10.0%
Math Operators
ValueCountFrequency (%)
4
57.1%
2
28.6%
1
 
14.3%
Geometric Shapes
ValueCountFrequency (%)
4
44.4%
2
22.2%
2
22.2%
1
 
11.1%
Hangul
ValueCountFrequency (%)
2
 
5.1%
2
 
5.1%
2
 
5.1%
1
 
2.6%
1
 
2.6%
1
 
2.6%
1
 
2.6%
1
 
2.6%
1
 
2.6%
1
 
2.6%
Other values (26) 26
66.7%
Diacriticals
ValueCountFrequency (%)
͠ 2
66.7%
͜ 1
33.3%
Emoticons
ValueCountFrequency (%)
😁 2
28.6%
😍 2
28.6%
🙊 1
14.3%
😃 1
14.3%
😊 1
14.3%
Georgian
ValueCountFrequency (%)
1
100.0%
Enclosed Alphanum Sup
ValueCountFrequency (%)
🇯 1
50.0%
🇵 1
50.0%
Misc Technical
ValueCountFrequency (%)
1
50.0%
1
50.0%
IPA Ext
ValueCountFrequency (%)
ʖ 1
100.0%
Enclosed Alphanum
ValueCountFrequency (%)
1
100.0%
Arabic
ValueCountFrequency (%)
٪ 1
100.0%

host_id
Real number (ℝ)

HIGH CORRELATION 

Distinct35399
Distinct (%)76.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84391858
Minimum2438
Maximum3.6245369 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size363.6 KiB
2023-09-05T22:53:11.579915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2438
5-th percentile908631.6
Q19194284
median37194964
Q31.3622744 × 108
95-th percentile3.0258672 × 108
Maximum3.6245369 × 108
Range3.6245125 × 108
Interquartile range (IQR)1.2703316 × 108

Descriptive statistics

Standard deviation99132599
Coefficient of variation (CV)1.1746702
Kurtosis0.197352
Mean84391858
Median Absolute Deviation (MAD)33768999
Skewness1.2080481
Sum3.9265 × 1012
Variance9.8272723 × 1015
MonotonicityNot monotonic
2023-09-05T22:53:11.669838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
107434423 249
 
0.5%
137358866 138
 
0.3%
61391963 118
 
0.3%
19303369 98
 
0.2%
334163301 83
 
0.2%
51501835 78
 
0.2%
48005494 77
 
0.2%
22541573 74
 
0.2%
204704622 72
 
0.2%
9419684 62
 
0.1%
Other values (35389) 45478
97.7%
ValueCountFrequency (%)
2438 1
 
< 0.1%
2571 1
 
< 0.1%
2782 3
< 0.1%
2787 7
< 0.1%
2845 2
 
< 0.1%
2868 1
 
< 0.1%
2881 2
 
< 0.1%
3415 1
 
< 0.1%
3563 1
 
< 0.1%
3647 2
 
< 0.1%
ValueCountFrequency (%)
362453686 1
< 0.1%
362419479 1
< 0.1%
362369539 1
< 0.1%
362241319 1
< 0.1%
362166123 1
< 0.1%
361977539 1
< 0.1%
361973567 1
< 0.1%
361814116 1
< 0.1%
361692734 1
< 0.1%
361596683 1
< 0.1%
Distinct11094
Distinct (%)23.9%
Missing35
Missing (%)0.1%
Memory size2.8 MiB
2023-09-05T22:53:11.829541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length37
Median length33
Mean length6.2124021
Min length1

Characters and Unicode

Total characters288827
Distinct characters213
Distinct categories15 ?
Distinct scripts7 ?
Distinct blocks10 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6645 ?
Unique (%)14.3%

Sample

1st rowJennifer
2nd rowLisaRoxanne
3rd rowGaron
4th rowRebecca
5th rowShunichi
ValueCountFrequency (%)
811
 
1.6%
and 623
 
1.2%
michael 434
 
0.8%
david 375
 
0.7%
john 312
 
0.6%
hotel 304
 
0.6%
alex 291
 
0.6%
daniel 269
 
0.5%
mike 258
 
0.5%
the 254
 
0.5%
Other values (10096) 48205
92.5%
2023-09-05T22:53:12.099000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 36157
 
12.5%
e 27817
 
9.6%
i 23612
 
8.2%
n 22740
 
7.9%
r 17161
 
5.9%
l 15022
 
5.2%
o 13069
 
4.5%
t 9388
 
3.3%
s 9099
 
3.2%
h 8822
 
3.1%
Other values (203) 105940
36.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 228979
79.3%
Uppercase Letter 52216
 
18.1%
Space Separator 5684
 
2.0%
Other Punctuation 1162
 
0.4%
Decimal Number 250
 
0.1%
Dash Punctuation 172
 
0.1%
Other Letter 121
 
< 0.1%
Open Punctuation 91
 
< 0.1%
Close Punctuation 90
 
< 0.1%
Math Symbol 46
 
< 0.1%
Other values (5) 16
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
5
 
4.1%
5
 
4.1%
5
 
4.1%
5
 
4.1%
3
 
2.5%
3
 
2.5%
3
 
2.5%
3
 
2.5%
3
 
2.5%
2
 
1.7%
Other values (69) 84
69.4%
Lowercase Letter
ValueCountFrequency (%)
a 36157
15.8%
e 27817
12.1%
i 23612
10.3%
n 22740
9.9%
r 17161
 
7.5%
l 15022
 
6.6%
o 13069
 
5.7%
t 9388
 
4.1%
s 9099
 
4.0%
h 8822
 
3.9%
Other values (52) 46092
20.1%
Uppercase Letter
ValueCountFrequency (%)
A 6146
 
11.8%
M 5068
 
9.7%
J 4981
 
9.5%
S 4498
 
8.6%
C 3456
 
6.6%
D 2678
 
5.1%
L 2638
 
5.1%
R 2453
 
4.7%
K 2413
 
4.6%
E 2284
 
4.4%
Other values (32) 15601
29.9%
Other Punctuation
ValueCountFrequency (%)
& 810
69.7%
. 247
 
21.3%
, 36
 
3.1%
' 30
 
2.6%
/ 25
 
2.2%
" 6
 
0.5%
@ 4
 
0.3%
: 2
 
0.2%
! 2
 
0.2%
Decimal Number
ValueCountFrequency (%)
3 62
24.8%
6 41
16.4%
4 36
14.4%
2 36
14.4%
5 20
 
8.0%
8 17
 
6.8%
7 14
 
5.6%
0 14
 
5.6%
1 10
 
4.0%
Math Symbol
ValueCountFrequency (%)
+ 37
80.4%
| 9
 
19.6%
Other Symbol
ValueCountFrequency (%)
2
66.7%
1
33.3%
Space Separator
ValueCountFrequency (%)
5684
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 172
100.0%
Open Punctuation
ValueCountFrequency (%)
( 91
100.0%
Close Punctuation
ValueCountFrequency (%)
) 90
100.0%
Final Punctuation
ValueCountFrequency (%)
9
100.0%
Format
ValueCountFrequency (%)
2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%
Currency Symbol
ValueCountFrequency (%)
£ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 281128
97.3%
Common 7511
 
2.6%
Han 106
 
< 0.1%
Cyrillic 67
 
< 0.1%
Hangul 7
 
< 0.1%
Hebrew 5
 
< 0.1%
Hiragana 3
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 36157
 
12.9%
e 27817
 
9.9%
i 23612
 
8.4%
n 22740
 
8.1%
r 17161
 
6.1%
l 15022
 
5.3%
o 13069
 
4.6%
t 9388
 
3.3%
s 9099
 
3.2%
h 8822
 
3.1%
Other values (70) 98241
34.9%
Han
ValueCountFrequency (%)
5
 
4.7%
5
 
4.7%
5
 
4.7%
5
 
4.7%
3
 
2.8%
3
 
2.8%
3
 
2.8%
3
 
2.8%
3
 
2.8%
2
 
1.9%
Other values (54) 69
65.1%
Common
ValueCountFrequency (%)
5684
75.7%
& 810
 
10.8%
. 247
 
3.3%
- 172
 
2.3%
( 91
 
1.2%
) 90
 
1.2%
3 62
 
0.8%
6 41
 
0.5%
+ 37
 
0.5%
, 36
 
0.5%
Other values (20) 241
 
3.2%
Cyrillic
ValueCountFrequency (%)
а 12
17.9%
и 8
11.9%
н 7
 
10.4%
л 4
 
6.0%
е 4
 
6.0%
А 4
 
6.0%
р 4
 
6.0%
к 2
 
3.0%
я 2
 
3.0%
з 2
 
3.0%
Other values (14) 18
26.9%
Hangul
ValueCountFrequency (%)
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
Hebrew
ValueCountFrequency (%)
ל 1
20.0%
א 1
20.0%
י 1
20.0%
נ 1
20.0%
ד 1
20.0%
Hiragana
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 288384
99.8%
None 241
 
0.1%
CJK 106
 
< 0.1%
Cyrillic 67
 
< 0.1%
Punctuation 11
 
< 0.1%
Hangul 7
 
< 0.1%
Hebrew 5
 
< 0.1%
Hiragana 3
 
< 0.1%
Misc Symbols 2
 
< 0.1%
Dingbats 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 36157
 
12.5%
e 27817
 
9.6%
i 23612
 
8.2%
n 22740
 
7.9%
r 17161
 
6.0%
l 15022
 
5.2%
o 13069
 
4.5%
t 9388
 
3.3%
s 9099
 
3.2%
h 8822
 
3.1%
Other values (67) 105497
36.6%
None
ValueCountFrequency (%)
é 101
41.9%
á 21
 
8.7%
í 20
 
8.3%
ë 15
 
6.2%
ü 10
 
4.1%
ı 9
 
3.7%
ó 8
 
3.3%
ê 7
 
2.9%
è 6
 
2.5%
ï 5
 
2.1%
Other values (19) 39
 
16.2%
Cyrillic
ValueCountFrequency (%)
а 12
17.9%
и 8
11.9%
н 7
 
10.4%
л 4
 
6.0%
е 4
 
6.0%
А 4
 
6.0%
р 4
 
6.0%
к 2
 
3.0%
я 2
 
3.0%
з 2
 
3.0%
Other values (14) 18
26.9%
Punctuation
ValueCountFrequency (%)
9
81.8%
2
 
18.2%
CJK
ValueCountFrequency (%)
5
 
4.7%
5
 
4.7%
5
 
4.7%
5
 
4.7%
3
 
2.8%
3
 
2.8%
3
 
2.8%
3
 
2.8%
3
 
2.8%
2
 
1.9%
Other values (54) 69
65.1%
Misc Symbols
ValueCountFrequency (%)
2
100.0%
Dingbats
ValueCountFrequency (%)
1
100.0%
Hangul
ValueCountFrequency (%)
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
Hebrew
ValueCountFrequency (%)
ל 1
20.0%
א 1
20.0%
י 1
20.0%
נ 1
20.0%
ד 1
20.0%
Hiragana
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

neighbourhood_group
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
Manhattan
20580 
Brooklyn
18632 
Queens
5791 
Bronx
 
1183
Staten Island
 
341

Length

Max length13
Median length9
Mean length8.1537602
Min length5

Characters and Unicode

Total characters379370
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManhattan
2nd rowBrooklyn
3rd rowBrooklyn
4th rowBrooklyn
5th rowManhattan

Common Values

ValueCountFrequency (%)
Manhattan 20580
44.2%
Brooklyn 18632
40.0%
Queens 5791
 
12.4%
Bronx 1183
 
2.5%
Staten Island 341
 
0.7%

Length

2023-09-05T22:53:12.200189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-05T22:53:12.290705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
manhattan 20580
43.9%
brooklyn 18632
39.8%
queens 5791
 
12.4%
bronx 1183
 
2.5%
staten 341
 
0.7%
island 341
 
0.7%

Most occurring characters

ValueCountFrequency (%)
n 67448
17.8%
a 62422
16.5%
t 41842
11.0%
o 38447
10.1%
M 20580
 
5.4%
h 20580
 
5.4%
B 19815
 
5.2%
r 19815
 
5.2%
l 18973
 
5.0%
y 18632
 
4.9%
Other values (10) 50816
13.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 332161
87.6%
Uppercase Letter 46868
 
12.4%
Space Separator 341
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 67448
20.3%
a 62422
18.8%
t 41842
12.6%
o 38447
11.6%
h 20580
 
6.2%
r 19815
 
6.0%
l 18973
 
5.7%
y 18632
 
5.6%
k 18632
 
5.6%
e 11923
 
3.6%
Other values (4) 13447
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
M 20580
43.9%
B 19815
42.3%
Q 5791
 
12.4%
S 341
 
0.7%
I 341
 
0.7%
Space Separator
ValueCountFrequency (%)
341
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 379029
99.9%
Common 341
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 67448
17.8%
a 62422
16.5%
t 41842
11.0%
o 38447
10.1%
M 20580
 
5.4%
h 20580
 
5.4%
B 19815
 
5.2%
r 19815
 
5.2%
l 18973
 
5.0%
y 18632
 
4.9%
Other values (9) 50475
13.3%
Common
ValueCountFrequency (%)
341
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 379370
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 67448
17.8%
a 62422
16.5%
t 41842
11.0%
o 38447
10.1%
M 20580
 
5.4%
h 20580
 
5.4%
B 19815
 
5.2%
r 19815
 
5.2%
l 18973
 
5.0%
y 18632
 
4.9%
Other values (10) 50816
13.4%
Distinct222
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
2023-09-05T22:53:12.418295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length26
Median length17
Mean length11.81054
Min length4

Characters and Unicode

Total characters549509
Distinct characters54
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st rowMidtown
2nd rowClinton Hill
3rd rowBedford-Stuyvesant
4th rowSunset Park
5th rowHell's Kitchen
ValueCountFrequency (%)
east 6129
 
8.2%
side 4311
 
5.7%
harlem 3504
 
4.7%
williamsburg 3486
 
4.6%
upper 3481
 
4.6%
heights 3458
 
4.6%
bedford-stuyvesant 3438
 
4.6%
village 2921
 
3.9%
west 2555
 
3.4%
bushwick 2225
 
3.0%
Other values (234) 39466
52.6%
2023-09-05T22:53:12.649348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 50804
 
9.2%
i 39418
 
7.2%
s 37584
 
6.8%
t 36671
 
6.7%
a 35536
 
6.5%
l 32223
 
5.9%
r 31525
 
5.7%
28447
 
5.2%
n 24845
 
4.5%
o 23311
 
4.2%
Other values (44) 209145
38.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 435611
79.3%
Uppercase Letter 79497
 
14.5%
Space Separator 28447
 
5.2%
Dash Punctuation 3953
 
0.7%
Other Punctuation 2001
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 50804
11.7%
i 39418
 
9.0%
s 37584
 
8.6%
t 36671
 
8.4%
a 35536
 
8.2%
l 32223
 
7.4%
r 31525
 
7.2%
n 24845
 
5.7%
o 23311
 
5.4%
d 18918
 
4.3%
Other values (15) 104776
24.1%
Uppercase Letter
ValueCountFrequency (%)
H 11308
14.2%
S 10717
13.5%
B 7787
9.8%
W 7674
9.7%
E 6661
 
8.4%
C 5248
 
6.6%
U 3554
 
4.5%
G 3473
 
4.4%
M 3033
 
3.8%
V 2960
 
3.7%
Other values (14) 17082
21.5%
Other Punctuation
ValueCountFrequency (%)
' 1864
93.2%
. 136
 
6.8%
, 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
28447
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3953
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 515108
93.7%
Common 34401
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 50804
 
9.9%
i 39418
 
7.7%
s 37584
 
7.3%
t 36671
 
7.1%
a 35536
 
6.9%
l 32223
 
6.3%
r 31525
 
6.1%
n 24845
 
4.8%
o 23311
 
4.5%
d 18918
 
3.7%
Other values (39) 184273
35.8%
Common
ValueCountFrequency (%)
28447
82.7%
- 3953
 
11.5%
' 1864
 
5.4%
. 136
 
0.4%
, 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 549509
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 50804
 
9.2%
i 39418
 
7.2%
s 37584
 
6.8%
t 36671
 
6.7%
a 35536
 
6.5%
l 32223
 
5.9%
r 31525
 
5.7%
28447
 
5.2%
n 24845
 
4.5%
o 23311
 
4.2%
Other values (44) 209145
38.1%

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct18831
Distinct (%)40.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.729563
Minimum40.50868
Maximum40.91169
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size363.6 KiB
2023-09-05T22:53:12.874478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum40.50868
5-th percentile40.645636
Q140.689945
median40.7244
Q340.76304
95-th percentile40.827285
Maximum40.91169
Range0.40301
Interquartile range (IQR)0.073095

Descriptive statistics

Standard deviation0.05497412
Coefficient of variation (CV)0.0013497351
Kurtosis0.12870231
Mean40.729563
Median Absolute Deviation (MAD)0.03659
Skewness0.2414261
Sum1895024.4
Variance0.0030221539
MonotonicityNot monotonic
2023-09-05T22:53:12.968941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.76411 37
 
0.1%
40.76076 27
 
0.1%
40.71579 24
 
0.1%
40.73837 23
 
< 0.1%
40.72179 23
 
< 0.1%
40.73756 22
 
< 0.1%
40.75544 21
 
< 0.1%
40.71813 16
 
< 0.1%
40.76211 16
 
< 0.1%
40.74871 16
 
< 0.1%
Other values (18821) 46302
99.5%
ValueCountFrequency (%)
40.50868 1
< 0.1%
40.52211 1
< 0.1%
40.52293 1
< 0.1%
40.53076 1
< 0.1%
40.53871 1
< 0.1%
40.53884 1
< 0.1%
40.53907 1
< 0.1%
40.54106 1
< 0.1%
40.54268 1
< 0.1%
40.54312 1
< 0.1%
ValueCountFrequency (%)
40.91169 1
< 0.1%
40.91055 1
< 0.1%
40.91051 1
< 0.1%
40.91031 1
< 0.1%
40.90804 1
< 0.1%
40.90734 1
< 0.1%
40.90659 1
< 0.1%
40.9059 1
< 0.1%
40.90578 1
< 0.1%
40.90491 1
< 0.1%

longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct14651
Distinct (%)31.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-73.950918
Minimum-74.23986
Maximum-73.71299
Zeros0
Zeros (%)0.0%
Negative46527
Negative (%)100.0%
Memory size363.6 KiB
2023-09-05T22:53:13.067214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-74.23986
5-th percentile-74.00329
Q1-73.98317
median-73.95538
Q3-73.93417
95-th percentile-73.859549
Maximum-73.71299
Range0.52687
Interquartile range (IQR)0.049

Descriptive statistics

Standard deviation0.047562221
Coefficient of variation (CV)-0.00064315931
Kurtosis4.5922732
Mean-73.950918
Median Absolute Deviation (MAD)0.02565
Skewness1.3568077
Sum-3440714.4
Variance0.0022621649
MonotonicityNot monotonic
2023-09-05T22:53:13.163309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-73.99371 42
 
0.1%
-73.99675 31
 
0.1%
-73.9535 29
 
0.1%
-73.98611 27
 
0.1%
-74.00588 22
 
< 0.1%
-73.97244 19
 
< 0.1%
-73.98702 18
 
< 0.1%
-73.95627 18
 
< 0.1%
-73.98276 17
 
< 0.1%
-73.99773 17
 
< 0.1%
Other values (14641) 46287
99.5%
ValueCountFrequency (%)
-74.23986 1
< 0.1%
-74.21238 1
< 0.1%
-74.20877 1
< 0.1%
-74.20295 1
< 0.1%
-74.19826 1
< 0.1%
-74.18305 1
< 0.1%
-74.18259 1
< 0.1%
-74.18028 1
< 0.1%
-74.17628 1
< 0.1%
-74.17459 1
< 0.1%
ValueCountFrequency (%)
-73.71299 1
< 0.1%
-73.7174 1
< 0.1%
-73.71928 1
< 0.1%
-73.72173 1
< 0.1%
-73.72179 1
< 0.1%
-73.72435 1
< 0.1%
-73.72506 1
< 0.1%
-73.72569 1
< 0.1%
-73.72582 1
< 0.1%
-73.72656 1
< 0.1%

room_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
Entire home/apt
23998 
Private room
21144 
Shared room
 
987
Hotel room
 
398

Length

Max length15
Median length15
Mean length13.509038
Min length10

Characters and Unicode

Total characters628535
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEntire home/apt
2nd rowEntire home/apt
3rd rowPrivate room
4th rowEntire home/apt
5th rowPrivate room

Common Values

ValueCountFrequency (%)
Entire home/apt 23998
51.6%
Private room 21144
45.4%
Shared room 987
 
2.1%
Hotel room 398
 
0.9%

Length

2023-09-05T22:53:13.244712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-05T22:53:13.320630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
entire 23998
25.8%
home/apt 23998
25.8%
room 22529
24.2%
private 21144
22.7%
shared 987
 
1.1%
hotel 398
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e 70525
11.2%
t 69538
11.1%
o 69454
11.1%
r 68658
10.9%
m 46527
 
7.4%
46527
 
7.4%
a 46129
 
7.3%
i 45142
 
7.2%
h 24985
 
4.0%
p 23998
 
3.8%
Other values (9) 117052
18.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 511483
81.4%
Space Separator 46527
 
7.4%
Uppercase Letter 46527
 
7.4%
Other Punctuation 23998
 
3.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 70525
13.8%
t 69538
13.6%
o 69454
13.6%
r 68658
13.4%
m 46527
9.1%
a 46129
9.0%
i 45142
8.8%
h 24985
 
4.9%
p 23998
 
4.7%
n 23998
 
4.7%
Other values (3) 22529
 
4.4%
Uppercase Letter
ValueCountFrequency (%)
E 23998
51.6%
P 21144
45.4%
S 987
 
2.1%
H 398
 
0.9%
Space Separator
ValueCountFrequency (%)
46527
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 23998
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 558010
88.8%
Common 70525
 
11.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 70525
12.6%
t 69538
12.5%
o 69454
12.4%
r 68658
12.3%
m 46527
8.3%
a 46129
8.3%
i 45142
8.1%
h 24985
 
4.5%
p 23998
 
4.3%
E 23998
 
4.3%
Other values (7) 69056
12.4%
Common
ValueCountFrequency (%)
46527
66.0%
/ 23998
34.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 628535
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 70525
11.2%
t 69538
11.1%
o 69454
11.1%
r 68658
10.9%
m 46527
 
7.4%
46527
 
7.4%
a 46129
 
7.3%
i 45142
 
7.2%
h 24985
 
4.0%
p 23998
 
3.8%
Other values (9) 117052
18.6%

price
Real number (ℝ)

SKEWED 

Distinct789
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean148.60567
Minimum0
Maximum10000
Zeros22
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size363.6 KiB
2023-09-05T22:53:13.399912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile38
Q165
median100
Q3160
95-th percentile350
Maximum10000
Range10000
Interquartile range (IQR)95

Descriptive statistics

Standard deviation318.62788
Coefficient of variation (CV)2.1441166
Kurtosis597.52175
Mean148.60567
Median Absolute Deviation (MAD)45
Skewness21.552553
Sum6914176
Variance101523.73
MonotonicityNot monotonic
2023-09-05T22:53:13.488677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 1709
 
3.7%
100 1673
 
3.6%
50 1390
 
3.0%
75 1205
 
2.6%
60 1182
 
2.5%
80 1112
 
2.4%
70 1095
 
2.4%
200 1020
 
2.2%
65 972
 
2.1%
120 962
 
2.1%
Other values (779) 34207
73.5%
ValueCountFrequency (%)
0 22
< 0.1%
9 1
 
< 0.1%
10 4
 
< 0.1%
11 1
 
< 0.1%
14 2
 
< 0.1%
15 4
 
< 0.1%
16 10
< 0.1%
17 6
 
< 0.1%
18 11
< 0.1%
19 21
< 0.1%
ValueCountFrequency (%)
10000 7
< 0.1%
9999 17
< 0.1%
9992 1
 
< 0.1%
9990 1
 
< 0.1%
9000 1
 
< 0.1%
8000 1
 
< 0.1%
7500 1
 
< 0.1%
7314 1
 
< 0.1%
7000 2
 
< 0.1%
6800 1
 
< 0.1%

minimum_nights
Real number (ℝ)

Distinct113
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5196338
Minimum1
Maximum1250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size363.6 KiB
2023-09-05T22:53:13.577346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q36
95-th percentile30
Maximum1250
Range1249
Interquartile range (IQR)4

Descriptive statistics

Standard deviation23.05057
Coefficient of variation (CV)2.7055822
Kurtosis623.96826
Mean8.5196338
Median Absolute Deviation (MAD)2
Skewness18.354597
Sum396393
Variance531.32876
MonotonicityNot monotonic
2023-09-05T22:53:13.669391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 11331
24.4%
2 9852
21.2%
3 7109
15.3%
30 4648
10.0%
5 3030
 
6.5%
4 3022
 
6.5%
7 2339
 
5.0%
14 793
 
1.7%
6 766
 
1.6%
10 572
 
1.2%
Other values (103) 3065
 
6.6%
ValueCountFrequency (%)
1 11331
24.4%
2 9852
21.2%
3 7109
15.3%
4 3022
 
6.5%
5 3030
 
6.5%
6 766
 
1.6%
7 2339
 
5.0%
8 129
 
0.3%
9 62
 
0.1%
10 572
 
1.2%
ValueCountFrequency (%)
1250 1
 
< 0.1%
1124 1
 
< 0.1%
1000 2
 
< 0.1%
999 1
 
< 0.1%
500 5
< 0.1%
480 1
 
< 0.1%
456 1
 
< 0.1%
400 2
 
< 0.1%
370 1
 
< 0.1%
366 1
 
< 0.1%

number_of_reviews
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct406
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.767167
Minimum0
Maximum746
Zeros10711
Zeros (%)23.0%
Negative0
Negative (%)0.0%
Memory size363.6 KiB
2023-09-05T22:53:13.759842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q321
95-th percentile116
Maximum746
Range746
Interquartile range (IQR)20

Descriptive statistics

Standard deviation46.6039
Coefficient of variation (CV)2.0469784
Kurtosis22.343367
Mean22.767167
Median Absolute Deviation (MAD)4
Skewness3.9426289
Sum1059288
Variance2171.9235
MonotonicityNot monotonic
2023-09-05T22:53:13.847344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10711
23.0%
1 5400
 
11.6%
2 3298
 
7.1%
3 2424
 
5.2%
4 1878
 
4.0%
5 1463
 
3.1%
6 1262
 
2.7%
7 1076
 
2.3%
8 935
 
2.0%
9 863
 
1.9%
Other values (396) 17217
37.0%
ValueCountFrequency (%)
0 10711
23.0%
1 5400
11.6%
2 3298
 
7.1%
3 2424
 
5.2%
4 1878
 
4.0%
5 1463
 
3.1%
6 1262
 
2.7%
7 1076
 
2.3%
8 935
 
2.0%
9 863
 
1.9%
ValueCountFrequency (%)
746 1
< 0.1%
695 1
< 0.1%
648 1
< 0.1%
629 1
< 0.1%
609 1
< 0.1%
561 1
< 0.1%
545 1
< 0.1%
544 1
< 0.1%
526 1
< 0.1%
522 1
< 0.1%

last_review
Date

MISSING 

Distinct2140
Distinct (%)6.0%
Missing10711
Missing (%)23.0%
Memory size363.6 KiB
Minimum2011-05-12 00:00:00
Maximum2020-08-16 00:00:00
2023-09-05T22:53:13.945002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:14.034876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

reviews_per_month
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct775
Distinct (%)2.2%
Missing10711
Missing (%)23.0%
Infinite0
Infinite (%)0.0%
Mean0.90580188
Minimum0.01
Maximum46.24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size363.6 KiB
2023-09-05T22:53:14.126736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.03
Q10.12
median0.38
Q31.21
95-th percentile3.4
Maximum46.24
Range46.23
Interquartile range (IQR)1.09

Descriptive statistics

Standard deviation1.2808978
Coefficient of variation (CV)1.4141037
Kurtosis63.436276
Mean0.90580188
Median Absolute Deviation (MAD)0.32
Skewness4.2289832
Sum32442.2
Variance1.6406991
MonotonicityNot monotonic
2023-09-05T22:53:14.213955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02 1393
 
3.0%
0.03 1161
 
2.5%
0.04 916
 
2.0%
0.05 866
 
1.9%
0.08 851
 
1.8%
0.06 808
 
1.7%
0.13 763
 
1.6%
0.07 655
 
1.4%
0.1 643
 
1.4%
0.09 611
 
1.3%
Other values (765) 27149
58.4%
(Missing) 10711
 
23.0%
ValueCountFrequency (%)
0.01 146
 
0.3%
0.02 1393
3.0%
0.03 1161
2.5%
0.04 916
2.0%
0.05 866
1.9%
0.06 808
1.7%
0.07 655
1.4%
0.08 851
1.8%
0.09 611
1.3%
0.1 643
1.4%
ValueCountFrequency (%)
46.24 1
< 0.1%
28.2 1
< 0.1%
22.2 1
< 0.1%
18.45 1
< 0.1%
17.2 1
< 0.1%
16.02 1
< 0.1%
14 2
< 0.1%
13.95 1
< 0.1%
13.5 1
< 0.1%
13.14 1
< 0.1%
Distinct53
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8904507
Minimum1
Maximum249
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size363.6 KiB
2023-09-05T22:53:14.299908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile19
Maximum249
Range248
Interquartile range (IQR)1

Descriptive statistics

Standard deviation22.731346
Coefficient of variation (CV)3.8590165
Kurtosis73.527813
Mean5.8904507
Median Absolute Deviation (MAD)0
Skewness7.950831
Sum274065
Variance516.71411
MonotonicityNot monotonic
2023-09-05T22:53:14.388941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 30586
65.7%
2 6196
 
13.3%
3 2595
 
5.6%
4 1344
 
2.9%
5 815
 
1.8%
6 486
 
1.0%
7 399
 
0.9%
8 392
 
0.8%
9 315
 
0.7%
249 249
 
0.5%
Other values (43) 3150
 
6.8%
ValueCountFrequency (%)
1 30586
65.7%
2 6196
 
13.3%
3 2595
 
5.6%
4 1344
 
2.9%
5 815
 
1.8%
6 486
 
1.0%
7 399
 
0.9%
8 392
 
0.8%
9 315
 
0.7%
10 210
 
0.5%
ValueCountFrequency (%)
249 249
0.5%
138 138
0.3%
118 118
0.3%
98 98
 
0.2%
83 83
 
0.2%
78 78
 
0.2%
77 77
 
0.2%
74 74
 
0.2%
72 72
 
0.2%
62 62
 
0.1%

availability_365
Real number (ℝ)

ZEROS 

Distinct366
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.14284
Minimum0
Maximum365
Zeros19967
Zeros (%)42.9%
Negative0
Negative (%)0.0%
Memory size363.6 KiB
2023-09-05T22:53:14.479414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median46
Q3252
95-th percentile365
Maximum365
Range365
Interquartile range (IQR)252

Descriptive statistics

Standard deviation142.51775
Coefficient of variation (CV)1.1764438
Kurtosis-1.1292728
Mean121.14284
Median Absolute Deviation (MAD)46
Skewness0.71924881
Sum5636413
Variance20311.309
MonotonicityNot monotonic
2023-09-05T22:53:14.566316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19967
42.9%
365 3231
 
6.9%
364 1343
 
2.9%
89 983
 
2.1%
179 861
 
1.9%
90 747
 
1.6%
180 665
 
1.4%
363 527
 
1.1%
88 480
 
1.0%
350 383
 
0.8%
Other values (356) 17340
37.3%
ValueCountFrequency (%)
0 19967
42.9%
1 274
 
0.6%
2 88
 
0.2%
3 69
 
0.1%
4 60
 
0.1%
5 61
 
0.1%
6 56
 
0.1%
7 62
 
0.1%
8 51
 
0.1%
9 45
 
0.1%
ValueCountFrequency (%)
365 3231
6.9%
364 1343
2.9%
363 527
 
1.1%
362 292
 
0.6%
361 131
 
0.3%
360 130
 
0.3%
359 168
 
0.4%
358 303
 
0.7%
357 105
 
0.2%
356 124
 
0.3%

Interactions

2023-09-05T22:53:09.525800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:02.599131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:03.332332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:04.108762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:04.906477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:05.664338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:06.403999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:07.157687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:08.048515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:08.777891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:09.597809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:02.678076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:03.405180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:04.183237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:04.976426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:05.734573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:06.473616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:07.231560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:08.117166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:08.847410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:09.677399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:02.754148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:03.485711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:04.267276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:05.056884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:05.811380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:06.553703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:07.312996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:08.195395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:08.926837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:09.761673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:02.833532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:03.570846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:04.351479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:05.139750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:05.892769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:06.635341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:07.398334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:08.275691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:09.008173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:09.838139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:02.905394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:03.649222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:04.431869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:05.214297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:05.966522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:06.711400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:07.475599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:08.348255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:09.084144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:09.911908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:02.973803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:03.723001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:04.507763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:05.287024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:06.036213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:06.783721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:07.553998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:08.417794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:09.155190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:09.987397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:03.044677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:03.800614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:04.588357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:05.363847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:06.110164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:06.857998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:07.631596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:08.489885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:09.230514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:10.067320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:03.120520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:03.879971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:04.670520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:05.441405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:06.186858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:06.935411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:07.821187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:08.565748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:09.307327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:10.138798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:03.187156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:03.952790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:04.746382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:05.512603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:06.254846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:07.007122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:07.892063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:08.632023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:09.377731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:10.213849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:03.257567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:04.027715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:04.823520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:05.587202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:06.326893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:07.080293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:07.968557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:08.702196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-05T22:53:09.448660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-09-05T22:53:14.643614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
idhost_idlatitudelongitudepriceminimum_nightsnumber_of_reviewsreviews_per_monthcalculated_host_listings_countavailability_365neighbourhood_grouproom_type
id1.0000.5550.0140.077-0.0670.016-0.3240.2260.1910.1740.0700.101
host_id0.5551.0000.0620.127-0.113-0.110-0.1210.2040.1850.1610.1090.150
latitude0.0140.0621.0000.0390.0910.024-0.052-0.0380.0420.0280.5410.106
longitude0.0770.1270.0391.000-0.381-0.1050.0890.1300.0440.0580.6470.136
price-0.067-0.1130.091-0.3811.0000.063-0.025-0.029-0.1230.0680.0220.038
minimum_nights0.016-0.1100.024-0.1050.0631.000-0.148-0.1820.1080.1280.0050.015
number_of_reviews-0.324-0.121-0.0520.089-0.025-0.1481.0000.8300.0130.1720.0330.024
reviews_per_month0.2260.204-0.0380.130-0.029-0.1820.8301.0000.1590.3590.0420.070
calculated_host_listings_count0.1910.1850.0420.044-0.1230.1080.0130.1591.0000.3860.1010.083
availability_3650.1740.1610.0280.0580.0680.1280.1720.3590.3861.0000.0750.077
neighbourhood_group0.0700.1090.5410.6470.0220.0050.0330.0420.1010.0751.0000.110
room_type0.1010.1500.1060.1360.0380.0150.0240.0700.0830.0770.1101.000

Missing values

2023-09-05T22:53:10.355823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-05T22:53:10.580010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-09-05T22:53:10.793374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
02595Skylit Midtown Castle2845JenniferManhattanMidtown40.75362-73.98377Entire home/apt1753482019-11-040.372365
13831Whole flr w/private bdrm, bath & kitchen(pls read)4869LisaRoxanneBrooklynClinton Hill40.68514-73.95976Entire home/apt7513402020-08-014.751265
25121BlissArtsSpace!7356GaronBrooklynBedford-Stuyvesant40.68688-73.95596Private room6029502019-12-020.371365
35136Spacious Brooklyn Duplex, Patio + Garden7378RebeccaBrooklynSunset Park40.66120-73.99423Entire home/apt1751412014-01-020.011295
45178Large Furnished Room Near B'way8967ShunichiManhattanHell's Kitchen40.76489-73.98493Private room6524732020-03-153.441340
55203Cozy Clean Guest Room - Family Apt7490MaryEllenManhattanUpper West Side40.80178-73.96723Private room7521182017-07-210.8910
65238Cute & Cozy Lower East Side 1 bdrm7549BenManhattanChinatown40.71344-73.99037Entire home/apt14011612019-07-291.214274
75552Spacious river view in the West Village8380MariaManhattanWest Village40.73552-74.01042Entire home/apt1603662019-08-100.491178
85803Lovely Room 1, Garden, Best Area, Legal rental9744LaurieBrooklynSouth Slope40.66829-73.98779Private room8841802020-03-181.313344
96021Wonderful Guest Bedroom in Manhattan CENTRAL PARK11528ClaudioManhattanUpper West Side40.79826-73.96113Private room8521232019-12-090.901365
idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
4651744802032A very large room in Williamsburg213793805OmerBrooklynWilliamsburg40.71970-73.95632Private room55140NaNNaN145
4651844802224Unique & beautiful design! Skylights-Washer/dryer143513836ElisabethBrooklynCrown Heights40.67302-73.91695Private room61300NaNNaN5363
46519448032011200 SF Duplex apt in an old shoe factory/ monthly921746Michael + JosuéBrooklynClinton Hill40.69034-73.96179Entire home/apt253300NaNNaN174
4652044804690Cozy place, peaceful, friendly and affordable.362419479EliaQueensSt. Albans40.69980-73.77104Private room4410NaNNaN1161
4652144807338Modern luxury Chelsea one bedroom36370873AshleyManhattanChelsea40.74698-73.99512Entire home/apt14070NaNNaN1358
4652244807522Designer Gramercy Studio Townhouse by UNSQ12941925BrianManhattanGramercy40.73433-73.98383Entire home/apt14570NaNNaN1164
4652344807786Cozy & comfy apt in the heart of Inwood Manhattan284790520SalarManhattanWashington Heights40.85820-73.92733Entire home/apt8760NaNNaN285
4652444811717Comfortable safe environment 24hr security camera362453686NicoleBrooklynEast Flatbush40.65399-73.93287Private room5930NaNNaN190
4652544814944Upper West Side studio 86th Street4039777FernandoManhattanUpper West Side40.78731-73.97029Entire home/apt80300NaNNaN1113
46526448180095MIN D/N trains, NEAR THE BEACH, 50’ TO MANHATTAN48098268MarinaBrooklynGravesend40.59945-73.98209Private room6610NaNNaN138